- 1 Introduction
- 2
GDCRNAToolspackage installation - 3 Data download
- 4 Data organization
- 5 Differential gene expression analysis
- 6 Competing endogenous RNAs network analysis
- 7 Univariate survival analysis
- 8 Functional enrichment analysis
- 9 sessionInfo
- References
1 Introduction
GDCRNATools is an R package which provides a standard, easy-to-use and comprehensive pipeline for downloading, organizing, and integrative analyzing RNA expression data in the GDC portal with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer.
Competing endogenous RNAs (ceRNAs) are RNAs that indirectly regulate other transcripts by competing for shared miRNAs. Although only a fraction of long non-coding RNAs has been functionally characterized, increasing evidences show that lncRNAs harboring multiple miRNA response elements (MREs) can act as ceRNAs to sequester miRNA activity and thus reduce the inhibition of miRNA on its targets. Deregulation of ceRNAs network may lead to human diseases.
The Genomic Data Commons (GDC) maintains standardized genomic, clinical, and biospecimen data from National Cancer Institute (NCI) programs including The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research To Generate Effective Treatments (TARGET), It also accepts high quality datasets from non-NCI supported cancer research programs, such as genomic data from the Foundation Medicine.
Many analyses can be perfomed using GDCRNATools, including differential gene expression analysis (limma(Ritchie et al. 2015), edgeR(Robinson, McCarthy, and Smyth 2010), and DESeq2(Love, Huber, and Anders 2014)), univariate survival analysis (CoxPH and KM), competing endogenous RNA network analysis (hypergeometric test, Pearson correlation analysis, regulation similarity analysis, sensitivity Pearson partial correlation(Paci, Colombo, and Farina 2014)), and functional enrichment analysis(GO, KEGG, DO). Besides some routine visualization methods such as volcano plot, scatter plot, and bubble plot, etc., three simple shiny apps are developed in GDCRNATools allowing users visualize the results on a local webpage. All the figures are plotted based on ggplot2 package unless otherwise specified.
This user-friendly package allows researchers perform the analysis by simply running a few functions and integrate their own pipelines such as molecular subtype classification, weighted correlation network analysis (WGCNA)(Langfelder and Horvath 2008), and TF-miRNA co-regulatory network analysis, etc. into the workflow easily. This could open a door to accelerate the study of crosstalk among different classes of RNAs and their regulatory relationships in cancer.
2 GDCRNATools package installation
The R software for running GDCRNATools can be downloaded from The Comprehensive R Archive Network (CRAN). The GDCRNATools package can be installed from Github.
devtools::install_github(repo='Jialab-UCR/GDCRNATools')library(GDCRNATools)3 Data download
Two methods are provided for downloading Gene Expression Quantification (HTSeq-Counts), Isoform Expression Quantification (BCGSC miRNA Profiling), and Clinical (Clinical Supplement) data:
Manual download
Step1: Download GDC Data Transfer Tool on the GDC website
Step2: Add data to the GDC cart, then download manifest file and metadata of the cart
Step3: Download data usinggdcRNADownload()function by providing the manifest fileAutomatic download
Download GDC Data Transfer Tool, manifest file, and data automatically by specifying theproject.idanddata.typeingdcRNADownload()function for RNAseq and miRNAs data, and ingdcClinicalDownload()function for clinical data
Users can also download data from GDC using the API method developed in TCGAbiolinks(Colaprico et al. 2016) or using TCGA-Assembler(Zhu, Qiu, and Ji 2014)
3.1 Manual download
3.1.1 Installation of GDC Data Transfer Tool gdc-client
Download GDC Data Transfer Tool from the GDC website and unzip the file
3.1.2 Download manifest file and metadata from GDC Data Portal
3.1.3 Download data
####### Download RNAseq data #######
gdcRNADownload(manifest = 'TCGA-PRAD/TCGA-PRAD.RNAseq.gdc_manifest.2017-11-23T14-40-52.txt',
directory = 'TCGA-PRAD/RNAseq')
####### Download miRNAs data #######
gdcRNADownload(manifest = 'TCGA-PRAD/TCGA-PRAD.miRNAs.gdc_manifest.2017-11-22T15-36-57.txt',
directory = 'TCGA-PRAD/miRNAs')
####### Download Clinical data #######
gdcRNADownload(manifest = 'TCGA-PRAD/TCGA-PRAD.Clinical.gdc_manifest.2017-11-23T14-42-01.txt',
directory = 'TCGA-PRAD/Clinical')3.2 Automatic download
gdcRNADownload()will download HTSeq-Counts data ifdata.type='RNAseq'and download BCGSC miRNA Profiling data ifdata.type='miRNAs'.project.idargument is required to be provided.gdcClinicalDownload()download clinical data in .xml format automatically by simply specifying theproject.idargument.
3.2.1 Download RNAseq/miRNAs data
####### Download RNAseq data #######
gdcRNADownload(project.id = 'TCGA-PRAD',
data.type = 'RNAseq',
write.manifest = TRUE,
directory = 'TCGA-PRAD/RNAseq')
####### Download miRNAs data #######
gdcRNADownload(project.id = 'TCGA-PRAD',
data.type = 'miRNAs',
write.manifest = TRUE,
directory = 'TCGA-PRAD/miRNAs')3.2.2 Download clinical data
####### Download clinical data #######
gdcClinicalDownload(project.id = 'TCGA-PRAD',
data.type = 'RNAseq',
write.manifest = TRUE,
directory = 'TCGA-PRAD/Clinical')4 Data organization
4.1 Parse metadata
Metadata can be parsed by either providing the metadata file that is downloaded in the data download step, or specifying the project.id and data.type in gdcParseMetadata() function to obtain information of data in the manifest file to facilitate data organization and basic clinical information of patients such as age, stage and gender, etc. for data analysis.
4.1.1 Parse metadata by providing the metadata file
####### Parse RNAseq metadata #######
metaMatrix.RNA <- gdcParseMetadata(metafile='TCGA-PRAD/TCGA-PRAD.RNAseq.metadata.2017-11-23T17-23-59.json')
####### Parse miRNAs metadata #######
metaMatrix.MIR <- gdcParseMetadata(metafile='TCGA-PRAD/TCGA-PRAD.miRNAs.metadata.2017-11-23T17-33-55.json')4.1.2 Parse metadata by specifying project.id and data.type
####### Parse RNAseq metadata #######
metaMatrix.RNA <- gdcParseMetadata(project.id = 'TCGA-PRAD',
data.type = 'RNAseq',
write.meta = TRUE)
metaMatrix.RNA[1:6,1:6]## file_name
## TCGA-2A-A8VL-01A d5b5e519-5ce4-4147-a500-25b2f442152d.htseq.counts.gz
## TCGA-2A-A8VO-01A ed22ecf9-2215-4bc4-a660-f8dbd2e2d15c.htseq.counts.gz
## TCGA-2A-A8VT-01A 4dd0008c-f544-438d-8802-e02dbf6c4a3e.htseq.counts.gz
## TCGA-2A-A8VV-01A 8f92f24b-90c7-46d0-b69d-e1d87a135a40.htseq.counts.gz
## TCGA-2A-A8VX-01A 6f421ec2-c74a-4719-b447-fab39c619d3b.htseq.counts.gz
## TCGA-2A-A8W1-01A 5d408d1f-e5e4-4901-b1a8-bf803e633117.htseq.counts.gz
## file_id patient
## TCGA-2A-A8VL-01A 2b760030-1cad-4554-931e-bac9205b56ca TCGA-2A-A8VL
## TCGA-2A-A8VO-01A 8c4da184-f1e1-4439-b501-8c3f88ba0d23 TCGA-2A-A8VO
## TCGA-2A-A8VT-01A 32eb6261-4d8b-4f8e-9435-cdbdc4766a3d TCGA-2A-A8VT
## TCGA-2A-A8VV-01A 8a880dc3-c706-4a16-9e1a-39adfdb7c72a TCGA-2A-A8VV
## TCGA-2A-A8VX-01A 2bacf214-6dfb-44e3-b5ab-ec51c343b24e TCGA-2A-A8VX
## TCGA-2A-A8W1-01A f6c810f2-344e-4e8e-8e7a-01c18ec72074 TCGA-2A-A8W1
## sample submitter_id
## TCGA-2A-A8VL-01A TCGA-2A-A8VL-01 TCGA-2A-A8VL-01A
## TCGA-2A-A8VO-01A TCGA-2A-A8VO-01 TCGA-2A-A8VO-01A
## TCGA-2A-A8VT-01A TCGA-2A-A8VT-01 TCGA-2A-A8VT-01A
## TCGA-2A-A8VV-01A TCGA-2A-A8VV-01 TCGA-2A-A8VV-01A
## TCGA-2A-A8VX-01A TCGA-2A-A8VX-01 TCGA-2A-A8VX-01A
## TCGA-2A-A8W1-01A TCGA-2A-A8W1-01 TCGA-2A-A8W1-01A
## entity_submitter_id
## TCGA-2A-A8VL-01A TCGA-2A-A8VL-01A-21R-A37L-07
## TCGA-2A-A8VO-01A TCGA-2A-A8VO-01A-11R-A37L-07
## TCGA-2A-A8VT-01A TCGA-2A-A8VT-01A-11R-A37L-07
## TCGA-2A-A8VV-01A TCGA-2A-A8VV-01A-11R-A37L-07
## TCGA-2A-A8VX-01A TCGA-2A-A8VX-01A-11R-A37L-07
## TCGA-2A-A8W1-01A TCGA-2A-A8W1-01A-11R-A37L-07####### Parse miRNAs metadata #######
metaMatrix.MIR <- gdcParseMetadata(project.id = 'TCGA-PRAD',
data.type = 'miRNAs',
write.meta = TRUE)
metaMatrix.MIR[1:6,1:6]## file_name
## TCGA-2A-A8VL-01A a8ad4b62-68e8-4d56-893e-e247a3099d94.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8VO-01A 22302d39-da19-4bfd-b4d8-aa951b9451a1.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8VT-01A de5cc4c2-2709-4bbe-8777-9d8e9cd56246.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8VV-01A f3402505-e2c1-4720-a9f2-f39105ad0327.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8VX-01A c2a2d423-e481-4821-9970-5e93d7d4442b.mirbase21.isoforms.quantification.txt
## TCGA-2A-A8W1-01A b1a5f1a4-a95a-4770-a234-709c4e9da1fe.mirbase21.isoforms.quantification.txt
## file_id patient
## TCGA-2A-A8VL-01A a0b6cbc1-43fa-4bed-83e8-917794158b98 TCGA-2A-A8VL
## TCGA-2A-A8VO-01A addea5e5-5b25-417c-bbb2-00438b8da4c6 TCGA-2A-A8VO
## TCGA-2A-A8VT-01A 7a337162-08ee-4600-96f5-79fed7b68898 TCGA-2A-A8VT
## TCGA-2A-A8VV-01A fc64fdd9-b679-4a97-bf5e-d757b64b252c TCGA-2A-A8VV
## TCGA-2A-A8VX-01A 8387f768-8d31-4ffa-88ae-dae0ef11b2fb TCGA-2A-A8VX
## TCGA-2A-A8W1-01A cb18f79c-41d4-4bb9-af6a-28e35b6a4470 TCGA-2A-A8W1
## sample submitter_id
## TCGA-2A-A8VL-01A TCGA-2A-A8VL-01 TCGA-2A-A8VL-01A
## TCGA-2A-A8VO-01A TCGA-2A-A8VO-01 TCGA-2A-A8VO-01A
## TCGA-2A-A8VT-01A TCGA-2A-A8VT-01 TCGA-2A-A8VT-01A
## TCGA-2A-A8VV-01A TCGA-2A-A8VV-01 TCGA-2A-A8VV-01A
## TCGA-2A-A8VX-01A TCGA-2A-A8VX-01 TCGA-2A-A8VX-01A
## TCGA-2A-A8W1-01A TCGA-2A-A8W1-01 TCGA-2A-A8W1-01A
## entity_submitter_id
## TCGA-2A-A8VL-01A TCGA-2A-A8VL-01A-21R-A37H-13
## TCGA-2A-A8VO-01A TCGA-2A-A8VO-01A-11R-A37H-13
## TCGA-2A-A8VT-01A TCGA-2A-A8VT-01A-11R-A37H-13
## TCGA-2A-A8VV-01A TCGA-2A-A8VV-01A-11R-A37H-13
## TCGA-2A-A8VX-01A TCGA-2A-A8VX-01A-11R-A37H-13
## TCGA-2A-A8W1-01A TCGA-2A-A8W1-01A-11R-A37H-134.2 Filter samples
4.2.1 Filter duplicated samples
Only one sample would be kept if the sample had been sequenced more than once by gdcFilterDuplicate().
####### Filter duplicated samples in RNAseq metadata #######
metaMatrix.RNA <- gdcFilterDuplicate(metaMatrix.RNA)## Removed 3 samples####### Filter duplicated samples in miRNAs metadata #######
metaMatrix.MIR <- gdcFilterDuplicate(metaMatrix.MIR)## Removed 4 samples4.2.2 Filter non-Primary Tumor and non-Solid Tissue Normal samples
Samples that are neither Primary Tumor (code: 01) nor Solid Tissue Normal (code: 11) would be filtered out by gdcFilterSampleType().
####### Filter non-Primary Tumor and non-Solid Tissue Normal samples in RNAseq metadata #######
metaMatrix.RNA <- gdcFilterSampleType(metaMatrix.RNA)## Removed 1 samples####### Filter non-Primary Tumor and non-Solid Tissue Normal samples in miRNAs metadata #######
metaMatrix.MIR <- gdcFilterSampleType(metaMatrix.MIR)## Removed 1 samples4.3 Merge data
gdcRNAMerge()merges raw counts data of RNAseq to a single expression matrix with rows are Ensembl id and columns are samples. Total read counts for 5p and 3p strands of miRNAs can be processed from isoform quantification files and then merged to a single expression matrix with rows are miRBase v21 identifiers and columns are samples.gdcClinicalMerge()merges clinical data to a dataframe with rows are patient id and columns are clinical traits. Ifkey.info=TRUE, only those most commonly used clinical traits will be reported, otherwise, all the clinical information will be reported.
4.3.1 Merge RNAseq/miRNAs data
####### Merge RNAseq data #######
rnaMatrix <- gdcRNAMerge(metadata = metaMatrix.RNA,
path = 'TCGA-PRAD/RNAseq/',
data.type = 'RNAseq')## ############### Merging RNAseq data ################
## ### This step may take a few minutes ###
## Number of samples: 547
## Number of genes: 60483rnaMatrix[1:6,1:6]## TCGA-2A-A8VL-01 TCGA-2A-A8VO-01 TCGA-2A-A8VT-01
## ENSG00000000003 2867 1667 3140
## ENSG00000000005 6 0 0
## ENSG00000000419 1354 888 1767
## ENSG00000000457 956 580 2163
## ENSG00000000460 119 91 305
## ENSG00000000938 159 171 228
## TCGA-2A-A8VV-01 TCGA-2A-A8VX-01 TCGA-2A-A8W1-01
## ENSG00000000003 3996 4869 2172
## ENSG00000000005 44 1 0
## ENSG00000000419 1408 1171 1593
## ENSG00000000457 1494 908 794
## ENSG00000000460 175 121 166
## ENSG00000000938 172 64 161####### Merge miRNAs data #######
mirMatrix <- gdcRNAMerge(metadata = metaMatrix.MIR,
path = 'TCGA-PRAD/miRNAs/',
data.type = 'miRNAs')## ############### Merging miRNAs data ###############
## Number of samples: 546
## Number of miRNAs: 2588mirMatrix[1:6,1:6]## TCGA-2A-A8VL-01 TCGA-2A-A8VO-01 TCGA-2A-A8VT-01
## hsa-let-7a-5p 130022 77195 170937
## hsa-let-7a-3p 133 84 91
## hsa-let-7a-2-3p 18 10 13
## hsa-let-7b-5p 68276 19131 36009
## hsa-let-7b-3p 78 30 55
## hsa-let-7c-5p 43015 22490 14099
## TCGA-2A-A8VV-01 TCGA-2A-A8VX-01 TCGA-2A-A8W1-01
## hsa-let-7a-5p 247370 73705 50261
## hsa-let-7a-3p 104 59 39
## hsa-let-7a-2-3p 13 3 4
## hsa-let-7b-5p 58349 17404 6663
## hsa-let-7b-3p 73 19 18
## hsa-let-7c-5p 36248 9694 117594.3.2 Merge clinical data
####### Merge clinical data #######
clinicalDa <- gdcClinicalMerge(path = 'TCGA-PRAD/Clinical/', key.info = TRUE)## ############### Merging Clinical data ###############clinicalDa[1:6,5:10]## clinical_stage clinical_T clinical_N clinical_M
## TCGA-EJ-5510 NA T1c NA M0
## TCGA-HC-8260 NA NA NA M0
## TCGA-Y6-A8TL NA T2a NA NA
## TCGA-V1-A8X3 NA T1c NA M0
## TCGA-VP-A87J NA T2a NA M0
## TCGA-KK-A6DY NA T1c NA M0
## gleason_grading gleason_score
## TCGA-EJ-5510 7433 7
## TCGA-HC-8260 734 7
## TCGA-Y6-A8TL 633 6
## TCGA-V1-A8X3 734 7
## TCGA-VP-A87J 734 7
## TCGA-KK-A6DY 734 74.4 TMM normalization and voom transformation
It has repeatedly shown that normalization is a critical way to ensure accurate estimation and detection of differential expression (DE) by removing systematic technical effects that occur in the data(Robinson and Oshlack 2010). TMM normalization is a simple and effective method for estimating relative RNA production levels from RNA-seq data. Voom is moreover faster and more convenient than existing RNA-seq methods, and converts RNA-seq data into a form that can be analyzed using similar tools as for microarrays(Law et al. 2014).
By running gdcVoomNormalization() function, raw counts data would be normalized by TMM method implemented in edgeR(Robinson, McCarthy, and Smyth 2010) and further transformed by the voom method provided in limma(Ritchie et al. 2015). Low expression genes (logcpm < 1 in more than half of the samples) will be filtered out by default. All the genes can be kept by setting filter=TRUE in the gdcVoomNormalization().
####### RNAseq data #######
rnaExpr <- gdcVoomNormalization(counts = rnaMatrix, filter = FALSE)
rnaExpr[1:6,1:6]## TCGA-2A-A8VL-01 TCGA-2A-A8VO-01 TCGA-2A-A8VT-01
## ENSG00000000003 5.891004 5.469541 5.675430
## ENSG00000000005 -2.894134 -6.233930 -6.941348
## ENSG00000000419 4.808971 4.561298 4.846146
## ENSG00000000457 4.307047 3.947222 5.137803
## ENSG00000000460 1.306293 1.281770 2.313680
## ENSG00000000938 1.722839 2.188135 1.894702
## TCGA-2A-A8VV-01 TCGA-2A-A8VX-01 TCGA-2A-A8W1-01
## ENSG00000000003 6.3329382 6.6613451 5.612615
## ENSG00000000005 -0.1558497 -5.0032503 -6.472525
## ENSG00000000419 4.8283607 4.6059284 5.165458
## ENSG00000000457 4.9138640 4.2391299 4.161378
## ENSG00000000460 1.8237441 1.3365997 1.906853
## ENSG00000000938 1.7988694 0.4230145 1.862865####### miRNAs data #######
mirExpr <- gdcVoomNormalization(counts = mirMatrix, filter = FALSE)
mirExpr[1:6,1:6]## TCGA-2A-A8VL-01 TCGA-2A-A8VO-01 TCGA-2A-A8VT-01
## hsa-let-7a-5p 14.676762 14.246607 15.773276
## hsa-let-7a-3p 4.749056 4.411257 4.905866
## hsa-let-7a-2-3p 1.897814 1.402695 2.145054
## hsa-let-7b-5p 13.747462 12.234040 13.526256
## hsa-let-7b-3p 3.982981 2.941115 4.184582
## hsa-let-7c-5p 13.080929 12.467406 12.173523
## TCGA-2A-A8VV-01 TCGA-2A-A8VX-01 TCGA-2A-A8W1-01
## hsa-let-7a-5p 15.705812 14.8712423 14.456561
## hsa-let-7a-3p 4.496858 4.5965754 4.143175
## hsa-let-7a-2-3p 1.544386 0.5091125 1.009320
## hsa-let-7b-5p 13.621931 12.7889304 11.541459
## hsa-let-7b-3p 3.989171 2.9871598 3.048848
## hsa-let-7c-5p 12.935132 11.9447084 12.3609345 Differential gene expression analysis
gdcDEAnalysis(), a convenience wrapper, provides three widely used methods limma(Ritchie et al. 2015), edgeR(Robinson, McCarthy, and Smyth 2010), and DESeq2(Love, Huber, and Anders 2014) to identify differentially expressed genes (DEGs) or miRNAs between any two groups defined by users. Note that DESeq2(Love, Huber, and Anders 2014) maybe slow with a single core. Multiple cores can be specified with the nCore argument if DESeq2(Love, Huber, and Anders 2014) is in use. Users are encouraged to consult the vignette of each method for more detailed information.
5.1 DE analysis
DEGAll <- gdcDEAnalysis(counts = rnaMatrix,
group = metaMatrix.RNA$sample_type,
comparison = 'PrimaryTumor-SolidTissueNormal',
method = 'limma')
DEGAll[1:6,]## symbol group logFC AveExpr t
## ENSG00000187699 C2orf88 protein_coding -2.657180 1.5056478 -19.46636
## ENSG00000176928 GCNT4 protein_coding -2.248112 0.5798701 -18.39206
## ENSG00000118298 CA14 protein_coding -2.630802 0.4748363 -17.57925
## ENSG00000103485 QPRT protein_coding -2.147259 1.9897483 -17.32704
## ENSG00000109667 SLC2A9 protein_coding -1.869863 1.6079446 -17.21612
## ENSG00000164764 SBSPON protein_coding -2.333725 2.5270242 -17.17468
## PValue FDR B
## ENSG00000187699 1.453473e-64 2.259715e-60 136.1299
## ENSG00000176928 3.303402e-59 2.567900e-55 123.6779
## ENSG00000118298 3.348976e-55 1.735551e-51 114.6210
## ENSG00000103485 5.729814e-54 2.227035e-50 111.9647
## ENSG00000109667 1.990189e-53 6.188294e-50 110.6756
## ENSG00000164764 3.166847e-53 8.205828e-50 110.29575.2 Report DE genes/miRNAs
All DEGs, DE long non-coding genes, DE protein coding genes and DE miRNAs could be reported separately by setting geneType argument in gdcDEReport(). Gene symbols and biotypes based on the Ensembl 90 annotation are reported in the output.
### All DEGs
deALL <- gdcDEReport(deg = DEGAll, gene.type = 'all')
#### DE long-noncoding
deLNC <- gdcDEReport(deg = DEGAll, gene.type = 'long_non_coding')
#### DE protein coding genes
dePC <- gdcDEReport(deg = DEGAll, gene.type = 'protein_coding')5.3 DEG visualization
Volcano plot and bar plot are used to visualize DE analysis results in different manners by gdcVolcanoPlot() and gdcBarPlot() functions, respectively . Hierarchical clustering on the expression matrix of DEGs can be analyzed and plotted by the gdcHeatmap() function.
5.3.1 Volcano plot
gdcVolcanoPlot(DEGAll)
5.3.2 Barplot
gdcBarPlot(deg = deALL, angle = 45, data.type = 'RNAseq')
5.3.3 Heatmap
Heatmap is generated based on the heatmap.2() function in gplots package.
degName = rownames(deALL)
gdcHeatmap(deg.id = degName, metadata = metaMatrix.RNA, rna.expr = rnaExpr)
6 Competing endogenous RNAs network analysis
Three criteria are used to determine the competing endogenous interactions between lncRNA-mRNA pairs:
- The lncRNA and mRNA must share significant number of miRNAs
- Expression of lncRNA and mRNA must be positively correlated
- Those common miRNAs should play similar roles in regulating the expression of lncRNA and mRNA
6.1 Hypergeometric test
Hypergenometric test is performed to test whether a lncRNA and mRNA share many miRNAs significantly.
A newly developed algorithm spongeScan(Furi’o-Tar’i et al. 2016) is used to predict MREs in lncRNAs acting as ceRNAs. Databases such as starBase v2.0(J.-H. Li et al. 2014), miRcode(Jeggari, Marks, and Larsson 2012) and mirTarBase release 7.0(Chou et al. 2017) are used to collect predicted and experimentally validated miRNA-mRNA and/or miRNA-lncRNA interactions. Gene IDs in these databases are updated to the latest Ensembl 90 annotation of human genome and miRNAs names are updated to the new release miRBase 21 identifiers. Users can also provide their own datasets of miRNA-lncRNA and miRNA-mRNA interactions.
The figure and equation below illustrate how the hypergeometric test works
\[p=1-\sum_{k=0}^m \frac{\binom{K}{k}\binom{N-K}{n-k}}{\binom{N}{n}} \] here \(m\) is the number of shared miRNAs, \(N\) is the total number of miRNAs in the database, \(n\) is the number of miRNAs targeting the lncRNA, \(K\) is the number of miRNAs targeting the protein coding gene.
6.2 Pearson correlation analysis
Pearson correlation coefficient is a measure of the strength of a linear association between two variables. As we all know, miRNAs are negative regulators of gene expression. If more common miRNAs are occupied by a lncRNA, less of them will bind to the target mRNA, thus increasing the expression level of mRNA. So expression of the lncRNA and mRNA in a ceRNA pair should be positively correlated.
6.3 Regulation pattern analysis
Two methods are used to measure the regulatory role of miRNAs on the lncRNA and mRNA:
- Regulation similarity
We defined a measurement regulation similarity score to check the similarity between miRNAs-lncRNA expression correlation and miRNAs-mRNA expression correlation.
\[Regulation\ similarity\ score = 1-\frac{1}{M} \sum_{k=1}^M [{\frac{|corr(m_k,l)-corr(m_k,g)|}{|corr(m_k,l)|+|corr(m_k,g)|}}]^M\]
where \(M\) is the total number of shared miRNAs, \(k\) is the \(k\)th shared miRNAs, \(corr(m_k, l)\) and \(corr(m_k, g)\) represents the Pearson correlation between the \(k\)th miRNA and lncRNA, the \(k\)th miRNA and mRNA, respectively
- Sensitivity correlation
Sensitivity correlation is defined by Paci et al.(Paci, Colombo, and Farina 2014) to measure if the correlation between a lncRNA and mRNA is mediated by a miRNA in the lncRNA-miRNA-mRNA triplet. We take average of all triplets of a lncRNA-mRNA pair and their shared miRNAs as the sensitivity correlation between a selected lncRNA and mRNA.
\[Sensitivity\ correlation = corr(l,g)-\frac{1}{M}\sum_{k=1}^M {\frac{corr(l,g)-corr(m_k,l)corr(m_k,g)}{\sqrt{1-corr(m_k,l)^2}\sqrt{1-corr(m_k,g)^2}}}\] where \(M\) is the total number of shared miRNAs, \(k\) is the \(k\)th shared miRNAs, \(corr(l,g)\), \(corr(m_k,l)\) and \(corr(m_k, g)\) represents the Pearson correlation between the long non-coding RNA and the protein coding gene, the kth miRNA and lncRNA, the kth miRNA and mRNA, respectively
The hypergeometric test of shared miRNAs, expression correlation analysis of lncRNA-mRNA pair, and regulation pattern analysis of shared miRNAs are all implemented in the gdcCEAnalysis() function.
ceOutput <- gdcCEAnalysis(lnc = rownames(deLNC),
pc = rownames(dePC),
lnc.targets = 'starBase',
pc.targets = 'starBase',
rna.expr = rnaExpr,
mir.expr = mirExpr)## Step 1/3: Hypergenometric test done !
## Step 2/3: Correlation analysis done !
## Step 3/3: Regulation pattern analysis done !ceOutput <- ceOutput[order(ceOutput$regSim),]
ceOutput[1:6,]## lncRNAs Genes Counts listTotal popHits popTotal
## 22 ENSG00000234456 ENSG00000163110 2 2 36 277
## 34 ENSG00000234456 ENSG00000043591 2 2 51 277
## 37 ENSG00000234456 ENSG00000119547 2 2 71 277
## 45 ENSG00000234456 ENSG00000112984 2 2 3 277
## 57 ENSG00000234456 ENSG00000184838 2 2 23 277
## 65 ENSG00000228223 ENSG00000129514 1 2 23 277
## foldEnrichment hyperPValue miRNAs
## 22 7.69444444444444 0.016480929210485 hsa-miR-374b-5p,hsa-miR-374a-5p
## 34 5.43137254901961 0.0333542614974101 hsa-miR-374b-5p,hsa-miR-374a-5p
## 37 3.90140845070423 0.0650081096635797 hsa-miR-374b-5p,hsa-miR-374a-5p
## 45 92.3333333333333 7.84806152880239e-05 hsa-miR-374b-5p,hsa-miR-374a-5p
## 57 12.0434782608696 0.00661853188929001 hsa-miR-374b-5p,hsa-miR-374a-5p
## 65 6.02173913043478 0.159446450060168 hsa-miR-590-3p
## cor corPValue regSim sppc
## 22 -0.3384664 1.0000000 0 0.0006305722
## 34 -0.4330765 1.0000000 0 0.0012113510
## 37 -0.3662249 1.0000000 0 -0.0241567080
## 45 -0.4187818 1.0000000 0 -0.0297453228
## 57 -0.2210811 0.9999999 0 -0.0155415582
## 65 -0.3084449 1.0000000 0 -0.00616044216.4 ceRNAs visualization
6.4.1 Correlation plot
gdcCorPlot(gene1 = 'ENSG00000234456',
gene2 = 'ENSG00000105971',
rna.expr = rnaExpr,
metadata = metaMatrix.RNA)
6.4.2 Correlation plot on a local webpage by shinyCorplot
Typing and running gdcCorPlot() for each pair of lncRNA-mRNA is bothering when multiple pairs are being interested in. shinyCorPlot() , a interactive plot function based on shiny package, can be easily operated by just clicking the genes in each drop down box (in the GUI window). By running shinyCorPlot() function, a local webpage would pop up and correlation plot between a lncRNA and mRNA would be automatically shown.
shinyCorPlot(gene1 = rownames(deLNC),
gene2 = rownames(dePC),
rna.expr = rnaExpr,
metadata = metaMatrix.RNA)
6.4.3 Network visulization in Cytoscape
lncRNA-miRNA-mRNA interactions can be reported by the gdcExportNetwork() and visualized in Cytoscape.
ceOutput2 <- ceOutput[ceOutput$hyperPValue<0.01 & ceOutput$corPValue<0.01 & ceOutput$regSim != 0,]
edges <- gdcExportNetwork(ceNetwork = ceOutput2, net = 'edges')
edges[1:6,]## fromNode toNode altNode1Name
## 1 ENSG00000234456 hsa-miR-374b-5p MAGI2-AS3
## 2 ENSG00000234456 hsa-miR-374a-5p MAGI2-AS3
## 3 ENSG00000245532 hsa-let-7i-5p NEAT1
## 4 ENSG00000245532 hsa-let-7e-5p NEAT1
## 5 ENSG00000245532 hsa-let-7g-5p NEAT1
## 6 ENSG00000245532 hsa-let-7f-5p NEAT1nodes <- gdcExportNetwork(ceNetwork = ceOutput2, net = 'nodes')
nodes[1:6,]## gene symbol type numInteractions
## 1 ENSG00000008300 CELSR3 pc 8
## 2 ENSG00000047597 XK pc 2
## 3 ENSG00000065320 NTN1 pc 2
## 4 ENSG00000065534 MYLK pc 2
## 5 ENSG00000066468 FGFR2 pc 2
## 6 ENSG00000075651 PLD1 pc 1
7 Univariate survival analysis
Two methods are provided to perform univariate survival analysis: Cox Proportional-Hazards (CoxPH) model and Kaplan Meier (KM) analysis based on the survival package. CoxPH model considers expression value as continous variable while KM analysis divides patients into high-expreesion and low-expression groups by a user-defined threshold such as median or mean. gdcSurvivalAnalysis() take a list of genes as input and report the hazard ratio, 95% confidence intervals, and test significance of each gene on overall survival.
7.1 CoxPH analysis
####### CoxPH analysis #######
survOutput <- gdcSurvivalAnalysis(gene = rownames(deALL),
method = 'coxph',
rna.expr = rnaExpr,
metadata = metaMatrix.RNA)
head(survOutput[order(survOutput$pValue),])## symbol coef HR lower95 upper95
## ENSG00000156804 FBXO32 -0.9061689 0.4040693 0.2444365 0.6679526
## ENSG00000273478 AC099676.1 1.8426288 6.3131126 1.9864365 20.0637629
## ENSG00000069535 MAOB -0.4870443 0.6144398 0.4517982 0.8356304
## ENSG00000128298 BAIAP2L2 0.4950804 1.6406302 1.1837845 2.2737816
## ENSG00000255545 AP004608.1 0.7108727 2.0357671 1.2702956 3.2625066
## ENSG00000180447 GAS1 -0.6253213 0.5350895 0.3530306 0.8110367
## pValue
## ENSG00000156804 0.0004100575
## ENSG00000273478 0.0017880566
## ENSG00000069535 0.0019053414
## ENSG00000128298 0.0029472842
## ENSG00000255545 0.0031344643
## ENSG00000180447 0.00320845887.2 KM analysis
####### KM analysis #######
survOutput <- gdcSurvivalAnalysis(gene = rownames(deALL),
method = 'KM',
rna.expr = rnaExpr,
metadata = metaMatrix.RNA,
sep = 'median')7.3 KM analysis visualization
7.3.1 KM plot
KM survival curves are ploted using the gdcKMPlot() function which is based on the R package survminer.
gdcKMPlot(gene = 'ENSG00000251321',
rna.expr = rnaExpr,
metadata = metaMatrix.RNA,
sep = 'median')
7.3.2 KM plot on a local webpage by shinyKMPlot
The shinyKMPlot() function is also a simply shiny app which allow users view KM plots of all genes of interests on a local webpackage conveniently.
shinyKMPlot(gene = rownames(deALL), rna.expr = rnaExpr, metadata = metaMatrix.RNA)
8 Functional enrichment analysis
One of the main uses of the GO is to perform enrichment analysis on gene sets. For example, given a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented (or under-represented) using annotations for that gene set and pathway enrichment can also be applied afterwards.
8.1 GO, KEGG and DO analyses
gdcEnrichAnalysis() can perform Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Disease Ontology (DO) functional enrichment analyses of a list of genes simultaneously. GO and KEGG analyses are based on the R/Bioconductor packages clusterProfilier(Yu et al. 2012) and DOSE(Yu et al. 2015). Redundant GO terms can be removed by specifying simplify=TRUE in the gdcEnrichAnalysis() function which uses the simplify() function in the clusterProfilier(Yu et al. 2012) package.
enrichOutput <- gdcEnrichAnalysis(gene = rownames(deALL), simplify = TRUE)## ### This step may take a few minutes ###
## Step 1/5: BP analysis done!
## Step 2/5: CC analysis done!
## Step 3/5: MF analysis done!
## Step 4/5: KEGG analysis done!
## Step 5/5: DO analysis done!terms <- c()
for (category in c('GO_BP', 'GO_CC', 'GO_MF', 'KEGG', 'DO')) {
terms <- c(terms, which(enrichOutput$Category==category)[1:3])
}
enrichOutput[terms,]## Terms Counts
## 1 GO:0006936~muscle contraction 77
## 2 GO:2000027~regulation of organ morphogenesis 47
## 3 GO:0051146~striated muscle cell differentiation 56
## 63 GO:0031012~extracellular matrix 91
## 64 GO:0043292~contractile fiber 55
## 65 GO:0042383~sarcolemma 38
## 79 GO:0005539~glycosaminoglycan binding 43
## 80 GO:0015267~channel activity 71
## 81 GO:0022803~passive transmembrane transporter activity 71
## 91 hsa05414~Dilated cardiomyopathy (DCM) 25
## 92 hsa05410~Hypertrophic cardiomyopathy (HCM) 22
## 93 hsa05412~Arrhythmogenic right ventricular cardiomyopathy (ARVC) 20
## 101 DOID:10283~prostate cancer 76
## 102 DOID:3856~male reproductive organ cancer 76
## 103 DOID:423~myopathy 69
## GeneRatio BgRatio pValue FDR foldEnrichment
## 1 77/1353 326/16447 9.881094e-18 4.882249e-14 2.871191
## 2 47/1353 185/16447 1.527429e-12 2.515676e-09 3.088268
## 3 56/1353 249/16447 2.588835e-12 2.741716e-09 2.733868
## 63 91/1431 425/17563 5.476425e-18 2.650589e-15 2.627916
## 64 55/1431 217/17563 1.472759e-14 2.376051e-12 3.110728
## 65 38/1431 124/17563 3.068355e-13 3.320284e-11 3.761153
## 79 43/1351 200/16514 3.287862e-09 2.689471e-06 2.628061
## 80 71/1351 450/16514 5.409995e-08 1.475125e-05 1.928603
## 81 71/1351 450/16514 5.409995e-08 1.475125e-05 1.928603
## 91 25/607 89/7174 4.416347e-08 1.245410e-05 3.319882
## 92 22/607 83/7174 8.615268e-07 1.139528e-04 3.132689
## 93 20/607 72/7174 1.212263e-06 1.139528e-04 3.282995
## 101 76/842 412/7577 3.902626e-06 2.993314e-03 1.659975
## 102 76/842 422/7577 9.701641e-06 3.720579e-03 1.620639
## 103 69/842 385/7577 2.987141e-05 5.727843e-03 1.612774
## geneID
## 1 ENSG00000087258/ENSG00000095303/ENSG00000159251/ENSG00000185532/ENSG00000163681/ENSG00000120907/ENSG00000141052/ENSG00000149575/ENSG00000105974/ENSG00000109846/ENSG00000145362/ENSG00000164171/ENSG00000183023/ENSG00000213366/ENSG00000198947/ENSG00000140416/ENSG00000018625/ENSG00000171873/ENSG00000123096/ENSG00000198363/ENSG00000152661/ENSG00000138119/ENSG00000154229/ENSG00000182963/ENSG00000035403/ENSG00000151617/ENSG00000131730/ENSG00000163794/ENSG00000122786/ENSG00000197043/ENSG00000136842/ENSG00000124205/ENSG00000065534/ENSG00000136160/ENSG00000089250/ENSG00000180616/ENSG00000171596/ENSG00000143153/ENSG00000156113/ENSG00000198523/ENSG00000108405/ENSG00000105976/ENSG00000077157/ENSG00000075073/ENSG00000182718/ENSG00000073756/ENSG00000134769/ENSG00000050628/ENSG00000095637/ENSG00000072952/ENSG00000108823/ENSG00000163431/ENSG00000004776/ENSG00000133392/ENSG00000101605/ENSG00000107796/ENSG00000135046/ENSG00000183963/ENSG00000213949/ENSG00000266964/ENSG00000162004/ENSG00000138735/ENSG00000163017/ENSG00000130176/ENSG00000101335/ENSG00000136546/ENSG00000143632/ENSG00000118729/ENSG00000187848/ENSG00000170425/ENSG00000175084/ENSG00000168398/ENSG00000198467/ENSG00000132932/ENSG00000150594/ENSG00000114854/ENSG00000196091
## 2 ENSG00000105707/ENSG00000128714/ENSG00000163637/ENSG00000107485/ENSG00000019549/ENSG00000140285/ENSG00000066468/ENSG00000070193/ENSG00000169071/ENSG00000176697/ENSG00000189120/ENSG00000171791/ENSG00000151617/ENSG00000244405/ENSG00000142632/ENSG00000139174/ENSG00000135925/ENSG00000128713/ENSG00000105976/ENSG00000128573/ENSG00000155760/ENSG00000101144/ENSG00000074527/ENSG00000134245/ENSG00000126778/ENSG00000072163/ENSG00000140807/ENSG00000016082/ENSG00000064300/ENSG00000125845/ENSG00000106819/ENSG00000154342/ENSG00000147655/ENSG00000146374/ENSG00000158055/ENSG00000104313/ENSG00000005513/ENSG00000122691/ENSG00000170577/ENSG00000164932/ENSG00000008300/ENSG00000107984/ENSG00000125931/ENSG00000089225/ENSG00000105989/ENSG00000184058/ENSG00000166823
## 3 ENSG00000105971/ENSG00000159251/ENSG00000077092/ENSG00000141052/ENSG00000164976/ENSG00000165410/ENSG00000161055/ENSG00000121577/ENSG00000163069/ENSG00000122367/ENSG00000106462/ENSG00000132819/ENSG00000165996/ENSG00000183023/ENSG00000198947/ENSG00000157168/ENSG00000140416/ENSG00000163110/ENSG00000134775/ENSG00000171791/ENSG00000130513/ENSG00000164530/ENSG00000136842/ENSG00000089250/ENSG00000139174/ENSG00000162614/ENSG00000105976/ENSG00000197168/ENSG00000024422/ENSG00000072195/ENSG00000160539/ENSG00000112276/ENSG00000163431/ENSG00000133392/ENSG00000086991/ENSG00000115641/ENSG00000128591/ENSG00000182389/ENSG00000167244/ENSG00000125845/ENSG00000143632/ENSG00000154277/ENSG00000118729/ENSG00000187848/ENSG00000154342/ENSG00000170558/ENSG00000047597/ENSG00000115884/ENSG00000107984/ENSG00000089225/ENSG00000171345/ENSG00000184058/ENSG00000187957/ENSG00000166823/ENSG00000169245/ENSG00000142178
## 63 ENSG00000164764/ENSG00000095713/ENSG00000197565/ENSG00000154736/ENSG00000183287/ENSG00000166833/ENSG00000165072/ENSG00000069702/ENSG00000143196/ENSG00000112293/ENSG00000066468/ENSG00000124479/ENSG00000070193/ENSG00000142910/ENSG00000120885/ENSG00000065618/ENSG00000262655/ENSG00000115380/ENSG00000198732/ENSG00000167346/ENSG00000165078/ENSG00000100234/ENSG00000124749/ENSG00000064205/ENSG00000183798/ENSG00000185585/ENSG00000172638/ENSG00000196104/ENSG00000125848/ENSG00000205221/ENSG00000135925/ENSG00000140479/ENSG00000182718/ENSG00000196878/ENSG00000079215/ENSG00000140682/ENSG00000184347/ENSG00000159674/ENSG00000101144/ENSG00000130203/ENSG00000173376/ENSG00000074527/ENSG00000134245/ENSG00000131981/ENSG00000132702/ENSG00000163815/ENSG00000132470/ENSG00000151914/ENSG00000152583/ENSG00000187122/ENSG00000185070/ENSG00000106278/ENSG00000123500/ENSG00000157150/ENSG00000188372/ENSG00000116962/ENSG00000156218/ENSG00000065320/ENSG00000087303/ENSG00000106819/ENSG00000106809/ENSG00000132561/ENSG00000124107/ENSG00000132386/ENSG00000154342/ENSG00000119699/ENSG00000156103/ENSG00000091986/ENSG00000113296/ENSG00000145113/ENSG00000108679/ENSG00000164932/ENSG00000187720/ENSG00000166670/ENSG00000100985/ENSG00000011465/ENSG00000100473/ENSG00000138829/ENSG00000112280/ENSG00000150893/ENSG00000105664/ENSG00000105989/ENSG00000167772/ENSG00000197614/ENSG00000160180/ENSG00000049089/ENSG00000133048/ENSG00000185303/ENSG00000034971/ENSG00000163810/ENSG00000139219
## 64 ENSG00000159251/ENSG00000120907/ENSG00000263155/ENSG00000165410/ENSG00000122367/ENSG00000121361/ENSG00000073712/ENSG00000144935/ENSG00000109846/ENSG00000145362/ENSG00000185567/ENSG00000183023/ENSG00000198947/ENSG00000140416/ENSG00000163110/ENSG00000134775/ENSG00000197321/ENSG00000152661/ENSG00000147166/ENSG00000162520/ENSG00000197361/ENSG00000058668/ENSG00000035403/ENSG00000129116/ENSG00000122786/ENSG00000136842/ENSG00000240771/ENSG00000089250/ENSG00000162614/ENSG00000156804/ENSG00000149596/ENSG00000077157/ENSG00000069431/ENSG00000154330/ENSG00000172403/ENSG00000182253/ENSG00000163431/ENSG00000133392/ENSG00000176749/ENSG00000101605/ENSG00000115641/ENSG00000151914/ENSG00000107796/ENSG00000157150/ENSG00000128591/ENSG00000101335/ENSG00000143632/ENSG00000157388/ENSG00000118729/ENSG00000175084/ENSG00000198467/ENSG00000164591/ENSG00000171345/ENSG00000114854/ENSG00000196091
## 65 ENSG00000163681/ENSG00000162989/ENSG00000120907/ENSG00000121577/ENSG00000163069/ENSG00000142583/ENSG00000120457/ENSG00000121361/ENSG00000145362/ENSG00000185567/ENSG00000183023/ENSG00000127990/ENSG00000198947/ENSG00000018625/ENSG00000123096/ENSG00000162520/ENSG00000058668/ENSG00000151617/ENSG00000158445/ENSG00000197043/ENSG00000089250/ENSG00000181856/ENSG00000143153/ENSG00000182718/ENSG00000134769/ENSG00000069431/ENSG00000154330/ENSG00000112276/ENSG00000108823/ENSG00000136425/ENSG00000135046/ENSG00000266964/ENSG00000162004/ENSG00000128591/ENSG00000157388/ENSG00000119699/ENSG00000175084/ENSG00000171345
## 79 ENSG00000188488/ENSG00000114631/ENSG00000119630/ENSG00000166833/ENSG00000069702/ENSG00000133800/ENSG00000140285/ENSG00000066468/ENSG00000070193/ENSG00000138685/ENSG00000112902/ENSG00000204381/ENSG00000127418/ENSG00000064205/ENSG00000196104/ENSG00000205221/ENSG00000118257/ENSG00000140479/ENSG00000113657/ENSG00000002726/ENSG00000184347/ENSG00000101670/ENSG00000101144/ENSG00000130203/ENSG00000137462/ENSG00000173376/ENSG00000132702/ENSG00000000971/ENSG00000072571/ENSG00000163815/ENSG00000187122/ENSG00000124875/ENSG00000106809/ENSG00000184613/ENSG00000147655/ENSG00000146374/ENSG00000091986/ENSG00000113296/ENSG00000011465/ENSG00000105664/ENSG00000169248/ENSG00000169245/ENSG00000012223
## 80 ENSG00000157551/ENSG00000105707/ENSG00000130529/ENSG00000162989/ENSG00000131620/ENSG00000171714/ENSG00000171227/ENSG00000169282/ENSG00000172005/ENSG00000115041/ENSG00000177119/ENSG00000149575/ENSG00000185760/ENSG00000150625/ENSG00000137672/ENSG00000120457/ENSG00000121361/ENSG00000137726/ENSG00000144935/ENSG00000159212/ENSG00000225697/ENSG00000185052/ENSG00000145936/ENSG00000107130/ENSG00000152661/ENSG00000164647/ENSG00000171791/ENSG00000182963/ENSG00000158445/ENSG00000151572/ENSG00000197043/ENSG00000133107/ENSG00000169504/ENSG00000156113/ENSG00000088836/ENSG00000149596/ENSG00000108405/ENSG00000069431/ENSG00000188910/ENSG00000083454/ENSG00000144481/ENSG00000157445/ENSG00000138449/ENSG00000169583/ENSG00000175538/ENSG00000171303/ENSG00000150995/ENSG00000141469/ENSG00000188372/ENSG00000169562/ENSG00000266964/ENSG00000182389/ENSG00000136546/ENSG00000157388/ENSG00000187848/ENSG00000099822/ENSG00000111319/ENSG00000213199/ENSG00000184156/ENSG00000166828/ENSG00000094755/ENSG00000162572/ENSG00000168447/ENSG00000171126/ENSG00000166206/ENSG00000120903/ENSG00000142185/ENSG00000146205/ENSG00000183960/ENSG00000001626/ENSG00000198785
## 81 ENSG00000157551/ENSG00000105707/ENSG00000130529/ENSG00000162989/ENSG00000131620/ENSG00000171714/ENSG00000171227/ENSG00000169282/ENSG00000172005/ENSG00000115041/ENSG00000177119/ENSG00000149575/ENSG00000185760/ENSG00000150625/ENSG00000137672/ENSG00000120457/ENSG00000121361/ENSG00000137726/ENSG00000144935/ENSG00000159212/ENSG00000225697/ENSG00000185052/ENSG00000145936/ENSG00000107130/ENSG00000152661/ENSG00000164647/ENSG00000171791/ENSG00000182963/ENSG00000158445/ENSG00000151572/ENSG00000197043/ENSG00000133107/ENSG00000169504/ENSG00000156113/ENSG00000088836/ENSG00000149596/ENSG00000108405/ENSG00000069431/ENSG00000188910/ENSG00000083454/ENSG00000144481/ENSG00000157445/ENSG00000138449/ENSG00000169583/ENSG00000175538/ENSG00000171303/ENSG00000150995/ENSG00000141469/ENSG00000188372/ENSG00000169562/ENSG00000266964/ENSG00000182389/ENSG00000136546/ENSG00000157388/ENSG00000187848/ENSG00000099822/ENSG00000111319/ENSG00000213199/ENSG00000184156/ENSG00000166828/ENSG00000094755/ENSG00000162572/ENSG00000168447/ENSG00000171126/ENSG00000166206/ENSG00000120903/ENSG00000142185/ENSG00000146205/ENSG00000183960/ENSG00000001626/ENSG00000198785
## 91 ENSG00000043591/ENSG00000077943/ENSG00000105855/ENSG00000108823/ENSG00000114854/ENSG00000115221/ENSG00000119699/ENSG00000132470/ENSG00000140416/ENSG00000144668/ENSG00000157388/ENSG00000157445/ENSG00000159251/ENSG00000161638/ENSG00000163069/ENSG00000164171/ENSG00000173175/ENSG00000175084/ENSG00000182389/ENSG00000183023/ENSG00000198467/ENSG00000198523/ENSG00000198947/ENSG00000213949/ENSG00000259207
## 92 ENSG00000077943/ENSG00000105855/ENSG00000108823/ENSG00000114854/ENSG00000115221/ENSG00000119699/ENSG00000132470/ENSG00000140416/ENSG00000144668/ENSG00000157388/ENSG00000157445/ENSG00000159251/ENSG00000161638/ENSG00000163069/ENSG00000164171/ENSG00000175084/ENSG00000182389/ENSG00000183023/ENSG00000198467/ENSG00000198947/ENSG00000213949/ENSG00000259207
## 93 ENSG00000077943/ENSG00000105855/ENSG00000108823/ENSG00000115221/ENSG00000132470/ENSG00000144668/ENSG00000152284/ENSG00000152661/ENSG00000157388/ENSG00000157445/ENSG00000161638/ENSG00000163069/ENSG00000164171/ENSG00000170558/ENSG00000175084/ENSG00000182389/ENSG00000183023/ENSG00000198947/ENSG00000213949/ENSG00000259207
## 101 ENSG00000019549/ENSG00000036672/ENSG00000073756/ENSG00000075213/ENSG00000076706/ENSG00000077092/ENSG00000084207/ENSG00000089685/ENSG00000100985/ENSG00000101144/ENSG00000101955/ENSG00000103175/ENSG00000105974/ENSG00000105976/ENSG00000106462/ENSG00000106483/ENSG00000109819/ENSG00000111837/ENSG00000114200/ENSG00000115884/ENSG00000115963/ENSG00000118971/ENSG00000122691/ENSG00000124225/ENSG00000125257/ENSG00000125845/ENSG00000125968/ENSG00000130203/ENSG00000130513/ENSG00000131981/ENSG00000132470/ENSG00000134184/ENSG00000134215/ENSG00000134602/ENSG00000136689/ENSG00000142515/ENSG00000142627/ENSG00000143546/ENSG00000144481/ENSG00000145113/ENSG00000145675/ENSG00000147889/ENSG00000148773/ENSG00000151632/ENSG00000156076/ENSG00000156103/ENSG00000156113/ENSG00000156970/ENSG00000159184/ENSG00000159263/ENSG00000161638/ENSG00000163220/ENSG00000164171/ENSG00000166851/ENSG00000167244/ENSG00000167346/ENSG00000167653/ENSG00000167751/ENSG00000169245/ENSG00000169710/ENSG00000169862/ENSG00000170962/ENSG00000171791/ENSG00000172005/ENSG00000176749/ENSG00000182718/ENSG00000185630/ENSG00000187210/ENSG00000196549/ENSG00000196924/ENSG00000197588/ENSG00000212993/ENSG00000225937/ENSG00000242110/ENSG00000259207/ENSG00000277893
## 102 ENSG00000019549/ENSG00000036672/ENSG00000073756/ENSG00000075213/ENSG00000076706/ENSG00000077092/ENSG00000084207/ENSG00000089685/ENSG00000100985/ENSG00000101144/ENSG00000101955/ENSG00000103175/ENSG00000105974/ENSG00000105976/ENSG00000106462/ENSG00000106483/ENSG00000109819/ENSG00000111837/ENSG00000114200/ENSG00000115884/ENSG00000115963/ENSG00000118971/ENSG00000122691/ENSG00000124225/ENSG00000125257/ENSG00000125845/ENSG00000125968/ENSG00000130203/ENSG00000130513/ENSG00000131981/ENSG00000132470/ENSG00000134184/ENSG00000134215/ENSG00000134602/ENSG00000136689/ENSG00000142515/ENSG00000142627/ENSG00000143546/ENSG00000144481/ENSG00000145113/ENSG00000145675/ENSG00000147889/ENSG00000148773/ENSG00000151632/ENSG00000156076/ENSG00000156103/ENSG00000156113/ENSG00000156970/ENSG00000159184/ENSG00000159263/ENSG00000161638/ENSG00000163220/ENSG00000164171/ENSG00000166851/ENSG00000167244/ENSG00000167346/ENSG00000167653/ENSG00000167751/ENSG00000169245/ENSG00000169710/ENSG00000169862/ENSG00000170962/ENSG00000171791/ENSG00000172005/ENSG00000176749/ENSG00000182718/ENSG00000185630/ENSG00000187210/ENSG00000196549/ENSG00000196924/ENSG00000197588/ENSG00000212993/ENSG00000225937/ENSG00000242110/ENSG00000259207/ENSG00000277893
## 103 ENSG00000007062/ENSG00000010704/ENSG00000013619/ENSG00000018236/ENSG00000022267/ENSG00000035403/ENSG00000043591/ENSG00000048740/ENSG00000049089/ENSG00000069431/ENSG00000069535/ENSG00000089250/ENSG00000089685/ENSG00000100985/ENSG00000101605/ENSG00000102024/ENSG00000104879/ENSG00000105974/ENSG00000105976/ENSG00000108823/ENSG00000109846/ENSG00000112280/ENSG00000112319/ENSG00000113580/ENSG00000114115/ENSG00000114854/ENSG00000120907/ENSG00000122367/ENSG00000123096/ENSG00000128591/ENSG00000130203/ENSG00000134363/ENSG00000134769/ENSG00000135218/ENSG00000136160/ENSG00000137077/ENSG00000137462/ENSG00000140416/ENSG00000143546/ENSG00000143632/ENSG00000147889/ENSG00000148346/ENSG00000149294/ENSG00000149596/ENSG00000150995/ENSG00000151617/ENSG00000152661/ENSG00000154229/ENSG00000158887/ENSG00000159251/ENSG00000159899/ENSG00000162520/ENSG00000163069/ENSG00000163071/ENSG00000164342/ENSG00000165410/ENSG00000169245/ENSG00000170558/ENSG00000171714/ENSG00000171791/ENSG00000175084/ENSG00000176697/ENSG00000177469/ENSG00000182253/ENSG00000196091/ENSG00000198467/ENSG00000198523/ENSG00000198947/ENSG00000280987
## geneSymbol
## 1 GNAO1/PTGS1/ACTC1/PRKG1/SLMAP/ADRA1A/MYOCD/SCN2B/CAV1/CRYAB/ANK2/ITGA2/SLC8A1/GSTM2/DMD/TPM1/ATP1A2/ADRA1D/SSPN/ASPH/GJA1/MYOF/PRKCA/GJC1/VCL/EDNRA/CKMT2/UCN/CALD1/ANXA6/TMOD1/EDN3/MYLK/EDNRB/NOS1/SSTR2/NMUR1/ATP1B1/KCNMA1/PLN/P2RX1/MET/PPP1R12B/TACR2/ANXA2/PTGS2/DTNA/PTGER3/SORBS1/MRVI1/SGCA/LMOD1/HSPB6/MYH11/MYOM1/ACTA2/ANXA1/SMTN/ITGA1/FXYD1/CCDC78/PDE5A/ACTG2/CNN1/MYL9/SCN7A/ACTA1/CASQ2/P2RX2/ADORA2B/DES/BDKRB2/TPM2/ATP8A2/ADRA2A/TNNC1/MYBPC1
## 2 HPN/HOXD13/PRICKLE2/GATA3/SNAI2/FGF7/FGFR2/FGF10/ROR2/BDNF/SP6/BCL2/EDNRA/ETV5/ARHGEF19/PRICKLE1/WNT10A/HOXD11/MET/FOXP2/FZD7/BMP7/NTN4/WNT2B/SIX1/LIMS2/NKD1/ISL1/NGFR/BMP2/ASPN/WNT3A/RSPO2/RSPO3/GRHL3/EYA1/SOX8/TWIST1/SIX2/CTHRC1/CELSR3/DKK1/CITED1/TBX5/WNT2/TBX1/MESP1
## 3 CAV2/ACTC1/RARB/MYOCD/KIAA1161/CFL2/SCGB3A1/POPDC2/SGCB/LDB3/EZH2/RBM38/HACD1/SLC8A1/DMD/NRG1/TPM1/PDLIM5/FHOD3/BCL2/GDF15/PI16/TMOD1/NOS1/PRICKLE1/NEXN/MET/NEK5/EHD2/SPEG/PLPP7/BVES/LMOD1/MYH11/NOX4/FHL2/FLNC/CACNB4/IGF2/BMP2/ACTA1/UCHL1/CASQ2/P2RX2/WNT3A/CDH2/XK/SDC1/DKK1/TBX5/KRT19/TBX1/DNER/MESP1/CXCL10/SIK1
## 63 SBSPON/CRTAC1/COL4A6/ADAMTS5/CCBE1/NAV2/MAMDC2/TGFBR3/DPT/GPLD1/FGFR2/NDP/FGF10/TINAGL1/CLU/COL17A1/SPON1/EFEMP1/SMOC1/MMP26/CPA6/TIMP3/COL21A1/WISP2/EMILIN3/OLFML2A/EFEMP2/SPOCK3/FLRT3/VIT/WNT10A/PCSK6/ANXA2/LAMB3/SLC1A3/TGFB1I1/SLIT3/SPON2/BMP7/APOE/NDNF/NTN4/WNT2B/LGALS3/HAPLN2/CLEC3B/ITGB4/DST/SPARCL1/SLIT1/FLRT2/PTPRZ1/COL10A1/TIMP4/ZP3/NID1/ADAMTSL3/NTN1/NID2/ASPN/OGN/MATN2/SLPI/SERPINF1/WNT3A/TGFB3/MMP16/CCDC80/THBS4/MUC4/LGALS3BP/CTHRC1/THSD4/MMP10/MMP9/DCN/COCH/FBN2/COL9A1/FREM2/COMP/WNT2/ANGPTL4/MFAP5/TFF3/COL9A2/CHI3L1/SFTPA2/MYOC/TGM4/COL2A1
## 64 ACTC1/ADRA1A/MYZAP/CFL2/LDB3/KCNJ8/FERMT2/TRPC1/CRYAB/ANK2/AHNAK2/SLC8A1/DMD/TPM1/PDLIM5/FHOD3/SVIL/GJA1/ITGB1BP2/SYNC/FBXL22/ATP2B4/VCL/PALLD/CALD1/TMOD1/ARHGEF25/NOS1/NEXN/FBXO32/JPH2/PPP1R12B/ABCC9/PGM5/SYNPO2/SYNM/LMOD1/MYH11/CDK5R1/MYOM1/FHL2/DST/ACTA2/TIMP4/FLNC/MYL9/ACTA1/CACNA1D/CASQ2/DES/TPM2/MYOZ3/KRT19/TNNC1/MYBPC1
## 65 SLMAP/KCNJ3/ADRA1A/POPDC2/SGCB/SLC2A5/KCNJ5/KCNJ8/ANK2/AHNAK2/SLC8A1/SGCE/DMD/ATP1A2/SSPN/SYNC/ATP2B4/EDNRA/KCNB1/ANXA6/NOS1/SLC2A4/ATP1B1/ANXA2/DTNA/ABCC9/PGM5/BVES/SGCA/CIB2/ANXA1/FXYD1/CCDC78/FLNC/CACNA1D/TGFB3/DES/KRT19
## 79 SERPINA5/PODXL2/PGF/NAV2/TGFBR3/LYVE1/FGF7/FGFR2/FGF10/FGF2/SEMA5A/LAYN/FGFRL1/WISP2/SPOCK3/VIT/NRP2/PCSK6/DPYSL3/AOC1/SLIT3/LIPG/BMP7/APOE/TLR2/NDNF/HAPLN2/CFH/HMMR/CLEC3B/SLIT1/CXCL6/OGN/NELL2/RSPO2/RSPO3/CCDC80/THBS4/DCN/COMP/CXCL11/CXCL10/LTF
## 80 KCNJ15/HPN/TRPM4/KCNJ3/ANO1/ANO5/TMEM37/KCNAB1/MAL/KCNIP3/ANO6/SCN2B/KCNQ5/GPM6A/TRPC6/KCNJ5/KCNJ8/FXYD6/TRPC1/CLIC6/SLC26A6/SLC24A3/KCNMB1/NCS1/GJA1/STEAP1/BCL2/GJC1/KCNB1/ANO4/ANXA6/TRPC4/CLIC4/KCNMA1/SLC4A11/JPH2/P2RX1/ABCC9/GJB3/P2RX5/TRPM8/CACNA2D3/SLC40A1/CLIC3/KCNE3/KCNK3/ITPR1/SLC14A1/ZP3/GJB1/FXYD1/CACNB4/SCN7A/CACNA1D/P2RX2/HCN2/SCNN1A/ASIC3/KCNQ3/SCNN1G/GABRP/SCNN1D/SCNN1B/KCNG3/GABRB3/CHRNA2/TRPM2/ANO7/KCNH8/CFTR/GRIN3A
## 81 KCNJ15/HPN/TRPM4/KCNJ3/ANO1/ANO5/TMEM37/KCNAB1/MAL/KCNIP3/ANO6/SCN2B/KCNQ5/GPM6A/TRPC6/KCNJ5/KCNJ8/FXYD6/TRPC1/CLIC6/SLC26A6/SLC24A3/KCNMB1/NCS1/GJA1/STEAP1/BCL2/GJC1/KCNB1/ANO4/ANXA6/TRPC4/CLIC4/KCNMA1/SLC4A11/JPH2/P2RX1/ABCC9/GJB3/P2RX5/TRPM8/CACNA2D3/SLC40A1/CLIC3/KCNE3/KCNK3/ITPR1/SLC14A1/ZP3/GJB1/FXYD1/CACNB4/SCN7A/CACNA1D/P2RX2/HCN2/SCNN1A/ASIC3/KCNQ3/SCNN1G/GABRP/SCNN1D/SCNN1B/KCNG3/GABRB3/CHRNA2/TRPM2/ANO7/KCNH8/CFTR/GRIN3A
## 91 ADRB1/ITGA8/ITGB8/SGCA/TNNC1/ITGB6/TGFB3/ITGB4/TPM1/ITGA9/CACNA1D/CACNA2D3/ACTC1/ITGA5/SGCB/ITGA2/ADCY5/DES/CACNB4/SLC8A1/TPM2/PLN/DMD/ITGA1/ITGB3
## 92 ITGA8/ITGB8/SGCA/TNNC1/ITGB6/TGFB3/ITGB4/TPM1/ITGA9/CACNA1D/CACNA2D3/ACTC1/ITGA5/SGCB/ITGA2/DES/CACNB4/SLC8A1/TPM2/DMD/ITGA1/ITGB3
## 93 ITGA8/ITGB8/SGCA/ITGB6/ITGB4/ITGA9/TCF7L1/GJA1/CACNA1D/CACNA2D3/ITGA5/SGCB/ITGA2/CDH2/DES/CACNB4/SLC8A1/DMD/ITGA1/ITGB3
## 101 SNAI2/USP2/PTGS2/SEMA3A/MCAM/RARB/GSTP1/BIRC5/MMP9/BMP7/SRPX/WFDC1/CAV1/MET/EZH2/SFRP4/PPARGC1A/MAK/BCHE/SDC1/RND3/CCND2/TWIST1/PMEPA1/ABCC4/BMP2/ID1/APOE/GDF15/LGALS3/ITGB4/GSTM1/VAV3/STK26/IL1RN/KLK3/EPHA2/S100A8/TRPM8/MUC4/PIK3R1/CDKN2A/MKI67/AKR1C2/WIF1/MMP16/KCNMA1/BUB1B/HOXB13/SIM2/ITGA5/S100A9/ITGA2/PLK1/IGF2/MMP26/PSCA/KLK2/CXCL10/FASN/CTNND2/PDGFD/BCL2/MAL/CDK5R1/ANXA2/PBX1/GCNT1/MME/FLNA/KLKP1/POU5F1B/PCA3/AMACR/ITGB3/SRD5A2
## 102 SNAI2/USP2/PTGS2/SEMA3A/MCAM/RARB/GSTP1/BIRC5/MMP9/BMP7/SRPX/WFDC1/CAV1/MET/EZH2/SFRP4/PPARGC1A/MAK/BCHE/SDC1/RND3/CCND2/TWIST1/PMEPA1/ABCC4/BMP2/ID1/APOE/GDF15/LGALS3/ITGB4/GSTM1/VAV3/STK26/IL1RN/KLK3/EPHA2/S100A8/TRPM8/MUC4/PIK3R1/CDKN2A/MKI67/AKR1C2/WIF1/MMP16/KCNMA1/BUB1B/HOXB13/SIM2/ITGA5/S100A9/ITGA2/PLK1/IGF2/MMP26/PSCA/KLK2/CXCL10/FASN/CTNND2/PDGFD/BCL2/MAL/CDK5R1/ANXA2/PBX1/GCNT1/MME/FLNA/KLKP1/POU5F1B/PCA3/AMACR/ITGB3/SRD5A2
## 103 PROM1/HFE/MAMLD1/CNTN1/FHL1/VCL/ADRB1/CELF2/COL9A2/ABCC9/MAOB/NOS1/BIRC5/MMP9/MYOM1/PLS3/CKM/CAV1/MET/SGCA/CRYAB/COL9A1/EYA4/NR3C1/RBP1/TNNC1/ADRA1A/LDB3/SSPN/FLNC/APOE/FST/DTNA/CD36/EDNRB/CCL21/TLR2/TPM1/S100A8/ACTA1/CDKN2A/LCN2/NCAM1/JPH2/ITPR1/EDNRA/GJA1/PRKCA/MPZ/ACTC1/NPR2/SYNC/SGCB/SPATA18/TLR3/CFL2/CXCL10/CDH2/ANO5/BCL2/DES/BDNF/CAVIN1/SYNM/MYBPC1/TPM2/PLN/DMD/MATR3
## Category
## 1 GO_BP
## 2 GO_BP
## 3 GO_BP
## 63 GO_CC
## 64 GO_CC
## 65 GO_CC
## 79 GO_MF
## 80 GO_MF
## 81 GO_MF
## 91 KEGG
## 92 KEGG
## 93 KEGG
## 101 DO
## 102 DO
## 103 DO8.2 Enrichment visualization
The output generated by gdcEnrichAnalysis() can be used for visualization in the gdcEnrichPlot() function by specifying type,category and numTerms arguments.
8.2.1 GO barplot
gdcEnrichPlot(enrichOutput, type = 'bar', category = 'GO', num.terms = 10)
8.2.2 GO bubble plot
gdcEnrichPlot(enrichOutput, type='bubble', category='GO', num.terms = 10)
8.2.3 KEGG/DO barplot
gdcEnrichPlot(enrichment = enrichOutput,
type = 'bar',
category = 'KEGG',
bar.color = 'chocolate1',
num.terms = 20)
gdcEnrichPlot(enrichment = enrichOutput,
type = 'bar',
category = 'DO',
bar.color = 'dodgerblue',
num.terms = 20)
8.2.4 KEGG/DO bubble plot
gdcEnrichPlot(=enrichOutput, category='KEGG',type = 'bubble', num.terms = 20)
gdcEnrichPlot(enrichOutput, category='DO',type = 'bubble', num.terms = 20)
8.2.5 Pathview
Users can visualize a pathway map with pathview() function in the pathview(Luo and Brouwer 2013) package. It displays related many-genes-to-many-terms on 2-D view, shows by genes on BioCarta & KEGG pathway maps. Gradient colors can be used to indicate if genes are up-regulated or down-regulated.
library(pathview)
deg <- deALL$logFC
names(deg) <- rownames(deALL)
hsa04022 <- pathview(gene.data = deg,
pathway.id = "hsa04022",
species = "hsa",
gene.idtype = 'ENSEMBL',
limit = list(gene=max(abs(geneList)), cpd=1))
8.2.6 View pathway maps on a local webpage by shinyPathview
shinyPathview() allows users view and download pathways of interests by simply selecting the pathway terms on a local webpage.
pathways <- as.character(enrichOutput$Terms[enrichOutput$Category=='KEGG'])
shinyPathview(deg, pathways = pathways, directory = 'pathview')
9 sessionInfo
sessionInfo()## R version 3.3.1 (2016-06-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.1 LTS
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] GDCRNATools_0.99.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.3-2 rjson_0.2.15
## [3] rprojroot_1.2 qvalue_2.4.2
## [5] htmlTable_1.9 XVector_0.12.1
## [7] GenomicRanges_1.24.3 base64enc_0.1-3
## [9] ggpubr_0.1.6 topGO_2.24.0
## [11] bit64_0.9-7 AnnotationDbi_1.34.4
## [13] splines_3.3.1 mnormt_1.5-5
## [15] GOSemSim_1.30.3 geneplotter_1.50.0
## [17] knitr_1.17 pathview_1.12.0
## [19] Formula_1.2-2 jsonlite_1.5
## [21] km.ci_0.5-2 broom_0.4.2
## [23] annotate_1.50.1 cluster_2.0.6
## [25] GO.db_3.3.0 png_0.1-7
## [27] graph_1.50.0 shiny_1.0.5
## [29] httr_1.3.1 backports_1.1.1
## [31] assertthat_0.2.0 Matrix_1.2-11
## [33] lazyeval_0.2.1 limma_3.28.21
## [35] acepack_1.4.1 htmltools_0.3.6
## [37] tools_3.3.1 bindrcpp_0.2
## [39] igraph_1.1.2 gtable_0.2.0
## [41] glue_1.2.0 reshape2_1.4.2
## [43] DO.db_2.9 dplyr_0.7.4
## [45] Rcpp_0.12.13 Biobase_2.32.0
## [47] Biostrings_2.40.2 gdata_2.18.0
## [49] nlme_3.1-131 psych_1.7.8
## [51] stringr_1.2.0 mime_0.5
## [53] clusterProfiler_3.0.5 gtools_3.5.0
## [55] XML_3.98-1.9 DOSE_2.10.7
## [57] org.Hs.eg.db_3.3.0 edgeR_3.14.0
## [59] zoo_1.8-0 zlibbioc_1.18.0
## [61] scales_0.5.0 parallel_3.3.1
## [63] SummarizedExperiment_1.2.3 KEGGgraph_1.30.0
## [65] SparseM_1.77 RColorBrewer_1.1-2
## [67] yaml_2.1.14 memoise_1.1.0
## [69] gridExtra_2.3 KMsurv_0.1-5
## [71] ggplot2_2.2.1 biomaRt_2.28.0
## [73] rpart_4.1-11 latticeExtra_0.6-28
## [75] stringi_1.1.5 RSQLite_2.0
## [77] genefilter_1.54.2 S4Vectors_0.10.3
## [79] checkmate_1.8.5 caTools_1.17.1
## [81] BiocGenerics_0.18.0 BiocParallel_1.6.6
## [83] GenomeInfoDb_1.8.7 rlang_0.1.4
## [85] pkgconfig_2.0.1 matrixStats_0.52.2
## [87] bitops_1.0-6 evaluate_0.10.1
## [89] lattice_0.20-35 purrr_0.2.4
## [91] bindr_0.1 labeling_0.3
## [93] cmprsk_2.2-7 htmlwidgets_0.9
## [95] tidyselect_0.2.3 bit_1.1-12
## [97] GSEABase_1.34.1 plyr_1.8.4
## [99] magrittr_1.5 DESeq2_1.12.4
## [101] R6_2.2.2 IRanges_2.6.1
## [103] gplots_3.0.1 Hmisc_4.0-3
## [105] DBI_0.7 foreign_0.8-69
## [107] prettydoc_0.2.0 survival_2.41-3
## [109] KEGGREST_1.12.3 RCurl_1.95-4.8
## [111] nnet_7.3-12 tibble_1.3.4
## [113] survMisc_0.5.4 KernSmooth_2.23-15
## [115] rmarkdown_1.7 locfit_1.5-9.1
## [117] grid_3.3.1 data.table_1.10.4-3
## [119] blob_1.1.0 Rgraphviz_2.16.0
## [121] digest_0.6.12 xtable_1.8-2
## [123] tidyr_0.7.2 httpuv_1.3.5
## [125] stats4_3.3.1 munsell_0.4.3
## [127] survminer_0.4.0References
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